When developing autonomous driving systems, driving simulations are often used to test various components (e.g. decision systems) that may be implemented as part of an autonomous driving vehicle. Accordingly, the quality of the simulation provides a direct relationship to the performance of the autonomous driving vehicle. Simulations are often developed using manually inputted driving scenarios that require extensive configuration efforts. Manual configuration of simulations, however, are understandably limited and often do not provide the potential driving scenarios that may be encountered in real life situations. In addition, complex data from an autonomous driving vehicle itself may also be analyzed. This information, however, is also difficult to gather as it requires a fleet of autonomous driving vehicles to capture the potentially limitless driving scenarios in which a vehicle may encounter. Accordingly, there is a continued need to improve methods in which driving scenarios for autonomous driving simulations are developed.